Analisis Sentimen Ulasan Google Play Store: Studi Komparatif Algoritma SVM, Naïve Bayes, dan Logistic Regression
DOI:
https://doi.org/10.37859/jf.v15i3.10016
Abstract
This research aims to compare Support Vector Machine (SVM), Naïve Bayes, and Logistic Regression methods in sentiment analysis of app reviews on Google Play Store to identify the best method based on accuracy, precision, recall, and F1-Score using 2000 GoPay and LinkAja reviews from Google Play Store respectively. The methodology consists of six stages, namely, data collection, labeling method evaluation, preprocessing evaluation, SMOTE testing to overcome imbalanced data, hyperparameter tuning optimization, and consistency validation with a combination of TF-IDF and three classification methods. The data were split using an 80:20 ratio, with 80% of the data used for training and 20% for testing. Experimental results show SVM gives the best performance with 93% accuracy, 92% precision, 93% recall, and 92% F1-Score on the GoPay dataset due to its ability to find the optimal hyperplane, followed by Logistic Regression with 92% accuracy and the third Naïve Bayes despite identical accuracy but showing greater bias towards the majority class. Validation using the LinkAja dataset proves SVM still maintains the best performance with 95% accuracy, so the research concludes SVM is the best method for sentiment analysis of app reviews on the Google Play Store which is proven to provide optimal and consistent performance
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